{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A tutorial for the whitebox Python package\n",
    "\n",
    "This notebook demonstrates the usage of the **whitebox** Python package for geospatial analysis, which is built on a stand-alone executable command-line program called [WhiteboxTools](https://github.com/jblindsay/whitebox-tools).\n",
    "\n",
    "* Authors: Dr. John Lindsay (http://www.uoguelph.ca/~hydrogeo/index.html)\n",
    "* Contributors: Dr. Qiusheng Wu (https://wetlands.io)\n",
    "* GitHub repo: https://github.com/giswqs/whitebox\n",
    "* WhiteboxTools: https://github.com/jblindsay/whitebox-tools\n",
    "* PyPI: https://pypi.org/project/whitebox/\n",
    "* Documentation: https://whitebox.readthedocs.io\n",
    "* Binder: https://gishub.org/whitebox-cloud\n",
    "* Free software: [MIT license](https://opensource.org/licenses/MIT)\n",
    "\n",
    "This tutorial can be accessed in three ways:\n",
    "\n",
    "* HTML version: https://gishub.org/whitebox-html\n",
    "* Viewable Notebook: https://gishub.org/whitebox-notebook\n",
    "* Interactive Notebook: https://gishub.org/whitebox-cloud\n",
    "\n",
    "**Launch this tutorial as an interactive Jupyter Notebook on the cloud - [MyBinder.org](https://gishub.org/whitebox-cloud).**\n",
    "\n",
    "![whitebox-gif](https://i.imgur.com/LF4UE1j.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Content\n",
    "\n",
    "* [Installation](#Installation)\n",
    "* [About whitebox](#About-whitebox)\n",
    "* [Getting data](#Getting-data)\n",
    "* [Using whitebox](#Using-whitebox)\n",
    "* [Displaying results](#Displaying-results)\n",
    "* [whitebox GUI](#whitebox-GUI)\n",
    "* [Citing whitebox](#Citing-whitebox)\n",
    "* [Credits](#Credits)\n",
    "* [Contact](#Contact)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "\n",
    "**whitebox** supports a variety of platforms, including Microsoft Windows, macOS, and Linux operating systems. Note that you will need to have **Python 3.x** installed. Python 2.x is not supported. The **whitebox** Python package can be installed using the following command:\n",
    "\n",
    "`pip install whitebox`\n",
    "\n",
    "If you have installed **whitebox** Python package before and want to upgrade to the latest version, you can use the following command:\n",
    "\n",
    "`pip install whitebox -U`\n",
    "\n",
    "If you encounter any installation issues, please check [Troubleshooting](https://github.com/giswqs/whitebox#troubleshooting) on the **whitebox** GitHub page and [Report Bugs](https://github.com/giswqs/whitebox#reporting-bugs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About whitebox\n",
    "\n",
    "**import whitebox and call WhiteboxTools()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The directory of this notebook: /home/jovyan/examples\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "root_dir = os.getcwd()  # get the directory where this notebook is located\n",
    "print('The directory of this notebook: {}'.format(root_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import whitebox\n",
    "wbt = whitebox.WhiteboxTools()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prints the whitebox-tools help...a listing of available commands**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "whitebox-tools Help\n",
      "\n",
      "The following commands are recognized:\n",
      "--cd, --wd       Changes the working directory; used in conjunction with --run flag.\n",
      "-h, --help       Prints help information.\n",
      "-l, --license    Prints the whitebox-tools license.\n",
      "--listtools      Lists all available tools. Keywords may also be used, --listtools slope.\n",
      "-r, --run        Runs a tool; used in conjuction with --wd flag; -r=\"LidarInfo\".\n",
      "--toolbox        Prints the toolbox associated with a tool; --toolbox=Slope.\n",
      "--toolhelp       Prints the help associated with a tool; --toolhelp=\"LidarInfo\".\n",
      "--toolparameters Prints the parameters (in json form) for a specific tool; --toolparameters=\"LidarInfo\".\n",
      "-v               Verbose mode. Without this flag, tool outputs will not be printed.\n",
      "--viewcode       Opens the source code of a tool in a web browser; --viewcode=\"LidarInfo\".\n",
      "--version        Prints the version information.\n",
      "\n",
      "Example Usage:\n",
      ">> ./whitebox-tools -r=lidar_info --cd=\"/path/to/data/\" -i=input.las --vlr --geokeys\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(wbt.help())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prints the whitebox-tools license**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "whitebox-tools License\n",
      "Copyright 2017-2018 John Lindsay\n",
      "\n",
      "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and\n",
      "associated documentation files (the \"Software\"), to deal in the Software without restriction,\n",
      "including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
      "and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,\n",
      "subject to the following conditions:\n",
      "\n",
      "The above copyright notice and this permission notice shall be included in all copies or substantial\n",
      "portions of the Software.\n",
      "\n",
      "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT\n",
      "NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n",
      "NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES\n",
      "OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\n",
      "CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(wbt.license())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prints the whitebox-tools version**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version information: whitebox-tools v0.11.0 by Dr. John B. Lindsay (c) 2017-2018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Version information: {}\".format(wbt.version()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Print the help for a specific tool.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElevPercentile\n",
      "Description:\n",
      "Calculates the elevation percentile raster from a DEM.\n",
      "Toolbox: Geomorphometric Analysis\n",
      "Parameters:\n",
      "\n",
      "Flag               Description\n",
      "-----------------  -----------\n",
      "-i, --input, --dem Input raster DEM file.\n",
      "-o, --output       Output raster file.\n",
      "--filterx          Size of the filter kernel in the x-direction.\n",
      "--filtery          Size of the filter kernel in the y-direction.\n",
      "--sig_digits       Number of significant digits.\n",
      "\n",
      "\n",
      "Example usage:\n",
      ">>./whitebox_tools -r=ElevPercentile -v --wd=\"/path/to/data/\" --dem=DEM.tif -o=output.tif --filter=25\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(wbt.tool_help(\"ElevPercentile\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tool names in the whitebox Python package can be called either using the snake_case or CamelCase convention (e.g. lidar_info or LidarInfo). The example below uses snake_case.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating normal vectors: 0.003435353S\n",
      "Smoothing normal vectors: 0.018674187S\n",
      "Updating elevations...\n",
      "Iteration 1 of 5...\n",
      "Iteration 2 of 5...\n",
      "Iteration 3 of 5...\n",
      "Iteration 4 of 5...\n",
      "Iteration 5 of 5...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os, pkg_resources\n",
    "\n",
    "# identify the sample data directory of the package\n",
    "data_dir = os.path.dirname(pkg_resources.resource_filename(\"whitebox\", 'testdata/'))\n",
    "\n",
    "# set whitebox working directory\n",
    "wbt.set_working_dir(data_dir)\n",
    "wbt.verbose = False\n",
    "\n",
    "# call whiteboxtools\n",
    "wbt.feature_preserving_denoise(\"DEM.tif\", \"smoothed.tif\", filter=9)\n",
    "wbt.breach_depressions(\"smoothed.tif\", \"breached.tif\")\n",
    "wbt.d_inf_flow_accumulation(\"breached.tif\", \"flow_accum.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**You can search tools using keywords. For example, the script below searches and lists tools with 'lidar' or 'LAS' in tool name or description.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "001 block_maximum: Creates a block-maximum raster from an input  ...\n",
      "002 block_minimum: Creates a block-minimum raster from an input  ...\n",
      "003 classify_overlap_points: Classifies or filters LAS points in regions o ...\n",
      "004 clip_lidar_to_polygon: Clips a LiDAR point cloud to a vector polygon ...\n",
      "005 erase_polygon_from_lidar: Erases (cuts out) a vector polygon or polygon ...\n",
      "006 filter_lidar_scan_angles: Removes points in a LAS file with scan angles ...\n",
      "007 find_flightline_edge_points: Identifies points along a flightline's edge i ...\n",
      "008 find_patch_or_class_edge_cells: Finds all cells located on the edge of patch  ...\n",
      "009 flightline_overlap: Reads a LiDAR (LAS) point file and outputs a  ...\n",
      "010 las_to_ascii: Converts one or more LAS files into ASCII tex ...\n",
      "011 las_to_multipoint_shapefile: Converts one or more LAS files into Multipoin ...\n",
      "012 lidar_colourize: Adds the red-green-blue colour fields of a Li ...\n",
      "013 lidar_construct_vector_tin: Creates a vector triangular irregular network ...\n",
      "014 lidar_elevation_slice: Outputs all of the points within a LiDAR (LAS ...\n",
      "015 lidar_ground_point_filter: Identifies ground points within LiDAR dataset ...\n",
      "016 lidar_hex_binning: Hex-bins a set of LiDAR points. ...\n",
      "017 lidar_hillshade: Calculates a hillshade value for points withi ...\n",
      "018 lidar_histogram: Creates a histogram from LiDAR data. ...\n",
      "019 lidar_idw_interpolation: Interpolates LAS files using an inverse-dista ...\n",
      "020 lidar_info: Prints information about a LiDAR (LAS) datase ...\n",
      "021 lidar_join: Joins multiple LiDAR (LAS) files into a singl ...\n",
      "022 lidar_kappa_index: Performs a kappa index of agreement (KIA) ana ...\n",
      "023 lidar_nearest_neighbour_gridding: Grids LAS files using nearest-neighbour schem ...\n",
      "024 lidar_point_density: Calculates the spatial pattern of point densi ...\n",
      "025 lidar_point_stats: Creates several rasters summarizing the distr ...\n",
      "026 lidar_remove_duplicates: Removes duplicate points from a LiDAR data se ...\n",
      "027 lidar_remove_outliers: Removes outliers (high and low points) in a L ...\n",
      "028 lidar_segmentation: Segments a LiDAR point cloud based on normal  ...\n",
      "029 lidar_segmentation_based_filter: Identifies ground points within LiDAR point c ...\n",
      "030 lidar_tin_gridding: Creates a raster grid based on a Delaunay tri ...\n",
      "031 lidar_thin: Thins a LiDAR point cloud, reducing point den ...\n",
      "032 lidar_thin_high_density: Thins points from high density areas within a ...\n",
      "033 lidar_tile: Tiles a LiDAR LAS file into multiple LAS file ...\n",
      "034 lidar_tile_footprint: Creates a vector polygon of the convex hull o ...\n",
      "035 lidar_tophat_transform: Performs a white top-hat transform on a Lidar ...\n",
      "036 normal_vectors: Calculates normal vectors for points within a ...\n",
      "037 pennock_landform_class: Classifies hillslope zones based on slope, pr ...\n",
      "038 raster_cell_assignment: Assign row or column number to cells. ...\n",
      "039 reclass: Reclassifies the values in a raster image. ...\n",
      "040 reclass_equal_interval: Reclassifies the values in a raster image bas ...\n",
      "041 reclass_from_file: Reclassifies the values in a raster image usi ...\n",
      "042 select_tiles_by_polygon: Copies LiDAR tiles overlapping with a polygon ...\n",
      "043 stream_link_class: Identifies the exterior/interior links and no ...\n"
     ]
    }
   ],
   "source": [
    "lidar_tools = wbt.list_tools(['lidar', 'LAS'])\n",
    "for index, tool in enumerate(lidar_tools):\n",
    "    print(\"{} {}: {} ...\".format(str(index+1).zfill(3), tool, lidar_tools[tool][:45]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List all available tools in whitebox-tools**. Currently, **whitebox** contains 372 tools. More tools will be added as they become available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "001 absolute_value: Calculates the absolute value of every cell i ...\n",
      "002 adaptive_filter: Performs an adaptive filter on an image. ...\n",
      "003 add: Performs an addition operation on two rasters ...\n",
      "004 add_point_coordinates_to_table: Modifies the attribute table of a point vecto ...\n",
      "005 aggregate_raster: Aggregates a raster to a lower resolution. ...\n",
      "006 and: Performs a logical AND operator on two Boolea ...\n",
      "007 anova: Performs an analysis of variance (ANOVA) test ...\n",
      "008 arc_cos: Returns the inverse cosine (arccos) of each v ...\n",
      "009 arc_sin: Returns the inverse sine (arcsin) of each val ...\n",
      "010 arc_tan: Returns the inverse tangent (arctan) of each  ...\n",
      "011 aspect: Calculates an aspect raster from an input DEM ...\n",
      "012 atan2: Returns the 2-argument inverse tangent (atan2 ...\n",
      "013 attribute_correlation: Performs a correlation analysis on attribute  ...\n",
      "014 attribute_histogram: Creates a histogram for the field values of a ...\n",
      "015 attribute_scattergram: Creates a scattergram for two field values of ...\n",
      "016 average_flowpath_slope: Measures the average slope gradient from each ...\n",
      "017 average_overlay: Calculates the average for each grid cell fro ...\n",
      "018 average_upslope_flowpath_length: Measures the average length of all upslope fl ...\n",
      "019 balance_contrast_enhancement: Performs a balance contrast enhancement on a  ...\n",
      "020 basins: Identifies drainage basins that drain to the  ...\n",
      "021 bilateral_filter: A bilateral filter is an edge-preserving smoo ...\n",
      "022 block_maximum: Creates a block-maximum raster from an input  ...\n",
      "023 block_minimum: Creates a block-minimum raster from an input  ...\n",
      "024 breach_depressions: Breaches all of the depressions in a DEM usin ...\n",
      "025 breach_single_cell_pits: Removes single-cell pits from an input DEM by ...\n",
      "026 buffer_raster: Maps a distance-based buffer around each non- ...\n",
      "027 ceil: Returns the smallest (closest to negative inf ...\n",
      "028 centroid: Calculates the centroid, or average location, ...\n",
      "029 centroid_vector: Identifes the centroid point of a vector poly ...\n",
      "030 change_vector_analysis: Performs a change vector analysis on a two-da ...\n",
      "031 classify_overlap_points: Classifies or filters LAS points in regions o ...\n",
      "032 clip_lidar_to_polygon: Clips a LiDAR point cloud to a vector polygon ...\n",
      "033 clip_raster_to_polygon: Clips a raster to a vector polygon. ...\n",
      "034 closing: A closing is a mathematical morphology operat ...\n",
      "035 clump: Groups cells that form physically discrete ar ...\n",
      "036 compactness_ratio: Calculates the compactness ratio (A/P), a mea ...\n",
      "037 conservative_smoothing_filter: Performs a conservative-smoothing filter on a ...\n",
      "038 construct_vector_tin: Creates a vector triangular irregular network ...\n",
      "039 convert_nodata_to_zero: Converts nodata values in a raster to zero. ...\n",
      "040 convert_raster_format: Converts raster data from one format to anoth ...\n",
      "041 corner_detection: Identifies corner patterns in boolean images  ...\n",
      "042 correct_vignetting: Corrects the darkening of images towards corn ...\n",
      "043 cos: Returns the cosine (cos) of each values in a  ...\n",
      "044 cosh: Returns the hyperbolic cosine (cosh) of each  ...\n",
      "045 cost_allocation: Identifies the source cell to which each grid ...\n",
      "046 cost_distance: Performs cost-distance accumulation on a cost ...\n",
      "047 cost_pathway: Performs cost-distance pathway analysis using ...\n",
      "048 count_if: Counts the number of occurrences of a specifi ...\n",
      "049 create_colour_composite: Creates a colour-composite image from three b ...\n",
      "050 create_hexagonal_vector_grid: Creates a hexagonal vector grid. ...\n",
      "051 create_plane: Creates a raster image based on the equation  ...\n",
      "052 create_rectangular_vector_grid: Creates a rectangular vector grid. ...\n",
      "053 crispness_index: Calculates the Crispness Index, which is used ...\n",
      "054 cross_tabulation: Performs a cross-tabulation on two categorica ...\n",
      "055 cumulative_distribution: Converts a raster image to its cumulative dis ...\n",
      "056 d8_flow_accumulation: Calculates a D8 flow accumulation raster from ...\n",
      "057 d8_mass_flux: Performs a D8 mass flux calculation. ...\n",
      "058 d8_pointer: Calculates a D8 flow pointer raster from an i ...\n",
      "059 d_inf_flow_accumulation: Calculates a D-infinity flow accumulation ras ...\n",
      "060 d_inf_mass_flux: Performs a D-infinity mass flux calculation. ...\n",
      "061 d_inf_pointer: Calculates a D-infinity flow pointer (flow di ...\n",
      "062 decrement: Decreases the values of each grid cell in an  ...\n",
      "063 depth_in_sink: Measures the depth of sinks (depressions) in  ...\n",
      "064 dev_from_mean_elev: Calculates deviation from mean elevation. ...\n",
      "065 diff_from_mean_elev: Calculates difference from mean elevation (eq ...\n",
      "066 diff_of_gaussian_filter: Performs a Difference of Gaussian (DoG) filte ...\n",
      "067 direct_decorrelation_stretch: Performs a direct decorrelation stretch enhan ...\n",
      "068 directional_relief: Calculates relief for cells in an input DEM f ...\n",
      "069 distance_to_outlet: Calculates the distance of stream grid cells  ...\n",
      "070 diversity_filter: Assigns each cell in the output grid the numb ...\n",
      "071 divide: Performs a division operation on two rasters  ...\n",
      "072 downslope_distance_to_stream: Measures distance to the nearest downslope st ...\n",
      "073 downslope_flowpath_length: Calculates the downslope flowpath length from ...\n",
      "074 downslope_index: Calculates the Hjerdt et al. (2004) downslope ...\n",
      "075 drainage_preserving_smoothing: Reduces short-scale variation in an input DEM ...\n",
      "076 edge_preserving_mean_filter: Performs a simple edge-preserving mean filter ...\n",
      "077 edge_proportion: Calculate the proportion of cells in a raster ...\n",
      "078 elev_above_pit: Calculate the elevation of each grid cell abo ...\n",
      "079 elev_percentile: Calculates the elevation percentile raster fr ...\n",
      "080 elev_relative_to_min_max: Calculates the elevation of a location relati ...\n",
      "081 elev_relative_to_watershed_min_max: Calculates the elevation of a location relati ...\n",
      "082 elevation_above_stream: Calculates the elevation of cells above the n ...\n",
      "083 elevation_above_stream_euclidean: Calculates the elevation of cells above the n ...\n",
      "084 eliminate_coincident_points: Removes any coincident, or nearly coincident, ...\n",
      "085 elongation_ratio: Calculates the elongation ratio for vector po ...\n",
      "086 emboss_filter: Performs an emboss filter on an image, simila ...\n",
      "087 equal_to: Performs a equal-to comparison operation on t ...\n",
      "088 erase_polygon_from_lidar: Erases (cuts out) a vector polygon or polygon ...\n",
      "089 erase_polygon_from_raster: Erases (cuts out) a vector polygon from a ras ...\n",
      "090 euclidean_allocation: Assigns grid cells in the output raster the v ...\n",
      "091 euclidean_distance: Calculates the Shih and Wu (2004) Euclidean d ...\n",
      "092 exp: Returns the exponential (base e) of values in ...\n",
      "093 exp2: Returns the exponential (base 2) of values in ...\n",
      "094 export_table_to_csv: Exports an attribute table to a CSV text file ...\n",
      "095 extend_vector_lines: Extends vector lines by a specified distance. ...\n",
      "096 extract_nodes: Converts vector lines or polygons into vertex ...\n",
      "097 extract_raster_statistics: Extracts descriptive statistics for a group o ...\n",
      "098 extract_raster_values_at_points: Extracts the values of raster(s) at vector po ...\n",
      "099 extract_streams: Extracts stream grid cells from a flow accumu ...\n",
      "100 extract_valleys: Identifies potential valley bottom grid cells ...\n",
      "101 fd8_flow_accumulation: Calculates an FD8 flow accumulation raster fr ...\n",
      "102 fd8_pointer: Calculates an FD8 flow pointer raster from an ...\n",
      "103 farthest_channel_head: Calculates the distance to the furthest upstr ...\n",
      "104 fast_almost_gaussian_filter: Performs a fast approximate Gaussian filter o ...\n",
      "105 feature_preserving_denoise: Reduces short-scale variation in an input DEM ...\n",
      "106 fetch_analysis: Performs an analysis of fetch or upwind dista ...\n",
      "107 fill_burn: Burns streams into a DEM using the FillBurn ( ...\n",
      "108 fill_depressions: Fills all of the depressions in a DEM. Depres ...\n",
      "109 fill_missing_data: Fills nodata holes in a DEM. ...\n",
      "110 fill_single_cell_pits: Raises pit cells to the elevation of their lo ...\n",
      "111 filter_lidar_scan_angles: Removes points in a LAS file with scan angles ...\n",
      "112 find_flightline_edge_points: Identifies points along a flightline's edge i ...\n",
      "113 find_lowest_or_highest_points: Locates the lowest and/or highest valued cell ...\n",
      "114 find_main_stem: Finds the main stem, based on stream lengths, ...\n",
      "115 find_no_flow_cells: Finds grid cells with no downslope neighbours ...\n",
      "116 find_parallel_flow: Finds areas of parallel flow in D8 flow direc ...\n",
      "117 find_patch_or_class_edge_cells: Finds all cells located on the edge of patch  ...\n",
      "118 find_ridges: Identifies potential ridge and peak grid cell ...\n",
      "119 flatten_lakes: Flattens lake polygons in a raster DEM. ...\n",
      "120 flightline_overlap: Reads a LiDAR (LAS) point file and outputs a  ...\n",
      "121 flip_image: Reflects an image in the vertical or horizont ...\n",
      "122 flood_order: Assigns each DEM grid cell its order in the s ...\n",
      "123 floor: Returns the largest (closest to positive infi ...\n",
      "124 flow_accumulation_full_workflow: Resolves all of the depressions in a DEM, out ...\n",
      "125 flow_length_diff: Calculates the local maximum absolute differe ...\n",
      "126 gamma_correction: Performs a sigmoidal contrast stretch on inpu ...\n",
      "127 gaussian_contrast_stretch: Performs a Gaussian contrast stretch on input ...\n",
      "128 gaussian_filter: Performs a Gaussian filter on an image. ...\n",
      "129 greater_than: Performs a greater-than comparison operation  ...\n",
      "130 hack_stream_order: Assigns the Hack stream order to each tributa ...\n",
      "131 high_pass_filter: Performs a high-pass filter on an input image ...\n",
      "132 high_pass_median_filter: Performs a high pass median filter on an inpu ...\n",
      "133 highest_position: Identifies the stack position of the maximum  ...\n",
      "134 hillshade: Calculates a hillshade raster from an input D ...\n",
      "135 hillslopes: Identifies the individual hillslopes draining ...\n",
      "136 histogram_equalization: Performs a histogram equalization contrast en ...\n",
      "137 histogram_matching: Alters the statistical distribution of a rast ...\n",
      "138 histogram_matching_two_images: This tool alters the cumulative distribution  ...\n",
      "139 hole_proportion: Calculates the proportion of the total area o ...\n",
      "140 horizon_angle: Calculates horizon angle (maximum upwind slop ...\n",
      "141 horton_stream_order: Assigns the Horton stream order to each tribu ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142 hypsometric_analysis: Calculates a hypsometric curve for one or mor ...\n",
      "143 idw_interpolation: Interpolates vector points into a raster surf ...\n",
      "144 ihs_to_rgb: Converts intensity, hue, and saturation (IHS) ...\n",
      "145 image_autocorrelation: Performs Moran's I analysis on two or more in ...\n",
      "146 image_correlation: Performs image correlation on two or more inp ...\n",
      "147 image_regression: Performs image regression analysis on two inp ...\n",
      "148 image_stack_profile: Plots an image stack profile (i.e. signature) ...\n",
      "149 impoundment_index: Calculates the impoundment size resulting fro ...\n",
      "150 in_place_add: Performs an in-place addition operation (inpu ...\n",
      "151 in_place_divide: Performs an in-place division operation (inpu ...\n",
      "152 in_place_multiply: Performs an in-place multiplication operation ...\n",
      "153 in_place_subtract: Performs an in-place subtraction operation (i ...\n",
      "154 increment: Increases the values of each grid cell in an  ...\n",
      "155 integer_division: Performs an integer division operation on two ...\n",
      "156 integral_image: Transforms an input image (summed area table) ...\n",
      "157 is_no_data: Identifies NoData valued pixels in an image. ...\n",
      "158 isobasins: Divides a landscape into nearly equal sized d ...\n",
      "159 jenson_snap_pour_points: Moves outlet points used to specify points of ...\n",
      "160 k_means_clustering: Performs a k-means clustering operation on a  ...\n",
      "161 k_nearest_mean_filter: A k-nearest mean filter is a type of edge-pre ...\n",
      "162 ks_test_for_normality: Evaluates whether the values in a raster are  ...\n",
      "163 kappa_index: Performs a kappa index of agreement (KIA) ana ...\n",
      "164 laplacian_filter: Performs a Laplacian filter on an image. ...\n",
      "165 laplacian_of_gaussian_filter: Performs a Laplacian-of-Gaussian (LoG) filter ...\n",
      "166 las_to_ascii: Converts one or more LAS files into ASCII tex ...\n",
      "167 las_to_multipoint_shapefile: Converts one or more LAS files into Multipoin ...\n",
      "168 layer_footprint: Creates a vector polygon footprint of the are ...\n",
      "169 lee_filter: Performs a Lee (Sigma) smoothing filter on an ...\n",
      "170 length_of_upstream_channels: Calculates the total length of channels upstr ...\n",
      "171 less_than: Performs a less-than comparison operation on  ...\n",
      "172 lidar_colourize: Adds the red-green-blue colour fields of a Li ...\n",
      "173 lidar_construct_vector_tin: Creates a vector triangular irregular network ...\n",
      "174 lidar_elevation_slice: Outputs all of the points within a LiDAR (LAS ...\n",
      "175 lidar_ground_point_filter: Identifies ground points within LiDAR dataset ...\n",
      "176 lidar_hex_binning: Hex-bins a set of LiDAR points. ...\n",
      "177 lidar_hillshade: Calculates a hillshade value for points withi ...\n",
      "178 lidar_histogram: Creates a histogram from LiDAR data. ...\n",
      "179 lidar_idw_interpolation: Interpolates LAS files using an inverse-dista ...\n",
      "180 lidar_info: Prints information about a LiDAR (LAS) datase ...\n",
      "181 lidar_join: Joins multiple LiDAR (LAS) files into a singl ...\n",
      "182 lidar_kappa_index: Performs a kappa index of agreement (KIA) ana ...\n",
      "183 lidar_nearest_neighbour_gridding: Grids LAS files using nearest-neighbour schem ...\n",
      "184 lidar_point_density: Calculates the spatial pattern of point densi ...\n",
      "185 lidar_point_stats: Creates several rasters summarizing the distr ...\n",
      "186 lidar_remove_duplicates: Removes duplicate points from a LiDAR data se ...\n",
      "187 lidar_remove_outliers: Removes outliers (high and low points) in a L ...\n",
      "188 lidar_segmentation: Segments a LiDAR point cloud based on normal  ...\n",
      "189 lidar_segmentation_based_filter: Identifies ground points within LiDAR point c ...\n",
      "190 lidar_tin_gridding: Creates a raster grid based on a Delaunay tri ...\n",
      "191 lidar_thin: Thins a LiDAR point cloud, reducing point den ...\n",
      "192 lidar_thin_high_density: Thins points from high density areas within a ...\n",
      "193 lidar_tile: Tiles a LiDAR LAS file into multiple LAS file ...\n",
      "194 lidar_tile_footprint: Creates a vector polygon of the convex hull o ...\n",
      "195 lidar_tophat_transform: Performs a white top-hat transform on a Lidar ...\n",
      "196 line_detection_filter: Performs a line-detection filter on an image. ...\n",
      "197 line_thinning: Performs line thinning a on Boolean raster im ...\n",
      "198 lines_to_polygons: Converts vector polylines to polygons. ...\n",
      "199 list_unique_values: Lists the unique values contained in a field  ...\n",
      "200 ln: Returns the natural logarithm of values in a  ...\n",
      "201 log10: Returns the base-10 logarithm of values in a  ...\n",
      "202 log2: Returns the base-2 logarithm of values in a r ...\n",
      "203 long_profile: Plots the stream longitudinal profiles for on ...\n",
      "204 long_profile_from_points: Plots the longitudinal profiles from flow-pat ...\n",
      "205 lowest_position: Identifies the stack position of the minimum  ...\n",
      "206 majority_filter: Assigns each cell in the output grid the most ...\n",
      "207 max: Performs a MAX operation on two rasters or a  ...\n",
      "208 max_absolute_overlay: Evaluates the maximum absolute value for each ...\n",
      "209 max_anisotropy_dev: Calculates the maximum anisotropy (directiona ...\n",
      "210 max_anisotropy_dev_signature: Calculates the anisotropy in deviation from m ...\n",
      "211 max_branch_length: Lindsay and Seibert's (2013) branch length in ...\n",
      "212 max_difference_from_mean: Calculates the maximum difference from mean e ...\n",
      "213 max_downslope_elev_change: Calculates the maximum downslope change in el ...\n",
      "214 max_elev_dev_signature: Calculates the maximum elevation deviation ov ...\n",
      "215 max_elevation_deviation: Calculates the maximum elevation deviation ov ...\n",
      "216 max_overlay: Evaluates the maximum value for each grid cel ...\n",
      "217 max_upslope_flowpath_length: Measures the maximum length of all upslope fl ...\n",
      "218 maximum_filter: Assigns each cell in the output grid the maxi ...\n",
      "219 mean_filter: Performs a mean filter (low-pass filter) on a ...\n",
      "220 median_filter: Performs a median filter on an input image. ...\n",
      "221 medoid: Calculates the medoid for a series of vector  ...\n",
      "222 min: Performs a MIN operation on two rasters or a  ...\n",
      "223 min_absolute_overlay: Evaluates the minimum absolute value for each ...\n",
      "224 min_downslope_elev_change: Calculates the minimum downslope change in el ...\n",
      "225 min_max_contrast_stretch: Performs a min-max contrast stretch on an inp ...\n",
      "226 min_overlay: Evaluates the minimum value for each grid cel ...\n",
      "227 minimum_bounding_box: Creates a vector minimum bounding rectangle a ...\n",
      "228 minimum_bounding_circle: Delineates the minimum bounding circle (i.e.  ...\n",
      "229 minimum_bounding_envelope: Creates a vector axis-aligned minimum boundin ...\n",
      "230 minimum_convex_hull: Creates a vector convex polygon around vector ...\n",
      "231 minimum_filter: Assigns each cell in the output grid the mini ...\n",
      "232 modified_k_means_clustering: Performs a modified k-means clustering operat ...\n",
      "233 modulo: Performs a modulo operation on two rasters or ...\n",
      "234 mosaic: Mosaics two or more images together. ...\n",
      "235 multi_part_to_single_part: Converts a vector file containing multi-part  ...\n",
      "236 multiply: Performs a multiplication operation on two ra ...\n",
      "237 multiscale_roughness: Calculates surface roughness over a range of  ...\n",
      "238 multiscale_roughness_signature: Calculates the surface roughness for points o ...\n",
      "239 multiscale_topographic_position_image: Creates a multiscale topographic position ima ...\n",
      "240 negate: Changes the sign of values in a raster or the ...\n",
      "241 new_raster_from_base: Creates a new raster using a base image. ...\n",
      "242 normal_vectors: Calculates normal vectors for points within a ...\n",
      "243 normalized_difference_vegetation_index: Calculates the normalized difference vegetati ...\n",
      "244 not: Performs a logical NOT operator on two Boolea ...\n",
      "245 not_equal_to: Performs a not-equal-to comparison operation  ...\n",
      "246 num_downslope_neighbours: Calculates the number of downslope neighbours ...\n",
      "247 num_inflowing_neighbours: Computes the number of inflowing neighbours t ...\n",
      "248 num_upslope_neighbours: Calculates the number of upslope neighbours t ...\n",
      "249 olympic_filter: Performs an olympic smoothing filter on an im ...\n",
      "250 opening: An opening is a mathematical morphology opera ...\n",
      "251 or: Performs a logical OR operator on two Boolean ...\n",
      "252 panchromatic_sharpening: Increases the spatial resolution of image dat ...\n",
      "253 pennock_landform_class: Classifies hillslope zones based on slope, pr ...\n",
      "254 percent_elev_range: Calculates percent of elevation range from a  ...\n",
      "255 percent_equal_to: Calculates the percentage of a raster stack t ...\n",
      "256 percent_greater_than: Calculates the percentage of a raster stack t ...\n",
      "257 percent_less_than: Calculates the percentage of a raster stack t ...\n",
      "258 percentage_contrast_stretch: Performs a percentage linear contrast stretch ...\n",
      "259 percentile_filter: Performs a percentile filter on an input imag ...\n",
      "260 perimeter_area_ratio: Calculates the perimeter-area ratio of vector ...\n",
      "261 pick_from_list: Outputs the value from a raster stack specifi ...\n",
      "262 plan_curvature: Calculates a plan (contour) curvature raster  ...\n",
      "263 polygon_area: Calculates the area of vector polygons. ...\n",
      "264 polygon_long_axis: This tool can be used to map the long axis of ...\n",
      "265 polygon_perimeter: Calculates the perimeter of vector polygons. ...\n",
      "266 polygon_short_axis: This tool can be used to map the short axis o ...\n",
      "267 polygons_to_lines: Converts vector polygons to polylines. ...\n",
      "268 power: Raises the values in grid cells of one raster ...\n",
      "269 prewitt_filter: Performs a Prewitt edge-detection filter on a ...\n",
      "270 principal_component_analysis: Performs a principal component analysis (PCA) ...\n",
      "271 print_geo_tiff_tags: Prints the tags within a GeoTIFF. ...\n",
      "272 profile: Plots profiles from digital surface models. ...\n",
      "273 profile_curvature: Calculates a profile curvature raster from an ...\n",
      "274 quantiles: Transforms raster values into quantiles. ...\n",
      "275 radius_of_gyration: Calculates the distance of cells from their p ...\n",
      "276 raise_walls: Raises walls in a DEM along a line or around  ...\n",
      "277 random_field: Creates an image containing random values. ...\n",
      "278 random_sample: Creates an image containing randomly located  ...\n",
      "279 range_filter: Assigns each cell in the output grid the rang ...\n",
      "280 raster_cell_assignment: Assign row or column number to cells. ...\n",
      "281 raster_histogram: Creates a histogram from raster values. ...\n",
      "282 raster_streams_to_vector: Converts a raster stream file into a vector f ...\n",
      "283 raster_summary_stats: Measures a rasters average, standard deviatio ...\n",
      "284 raster_to_vector_points: Converts a raster dataset to a vector of the  ...\n",
      "285 rasterize_streams: Rasterizes vector streams based on Lindsay (2 ...\n",
      "286 reciprocal: Returns the reciprocal (i.e. 1 / z) of values ...\n",
      "287 reclass: Reclassifies the values in a raster image. ...\n",
      "288 reclass_equal_interval: Reclassifies the values in a raster image bas ...\n",
      "289 reclass_from_file: Reclassifies the values in a raster image usi ...\n",
      "290 reinitialize_attribute_table: Reinitializes a vector's attribute table dele ...\n",
      "291 related_circumscribing_circle: Calculates the related circumscribing circle  ...\n",
      "292 relative_aspect: Calculates relative aspect (relative to a use ...\n",
      "293 relative_stream_power_index: Calculates the relative stream power index. ...\n",
      "294 relative_topographic_position: Calculates the relative topographic position  ...\n",
      "295 remove_off_terrain_objects: Removes off-terrain objects from a raster dig ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "296 remove_polygon_holes: Removes holes within the features of a vector ...\n",
      "297 remove_short_streams: Removes short first-order streams from a stre ...\n",
      "298 remove_spurs: Removes the spurs (pruning operation) from a  ...\n",
      "299 resample: Resamples one or more input images into a des ...\n",
      "300 rescale_value_range: Performs a min-max contrast stretch on an inp ...\n",
      "301 rgb_to_ihs: Converts red, green, and blue (RGB) images in ...\n",
      "302 rho8_pointer: Calculates a stochastic Rho8 flow pointer ras ...\n",
      "303 roberts_cross_filter: Performs a Robert's cross edge-detection filt ...\n",
      "304 root_mean_square_error: Calculates the RMSE and other accuracy statis ...\n",
      "305 round: Rounds the values in an input raster to the n ...\n",
      "306 ruggedness_index: Calculates the Riley et al.'s (1999) terrain  ...\n",
      "307 scharr_filter: Performs a Scharr edge-detection filter on an ...\n",
      "308 sediment_transport_index: Calculates the sediment transport index. ...\n",
      "309 select_tiles_by_polygon: Copies LiDAR tiles overlapping with a polygon ...\n",
      "310 set_nodata_value: Assign a specified value in an input image to ...\n",
      "311 shape_complexity_index: Calculates overall polygon shape complexity o ...\n",
      "312 shreve_stream_magnitude: Assigns the Shreve stream magnitude to each l ...\n",
      "313 sigmoidal_contrast_stretch: Performs a sigmoidal contrast stretch on inpu ...\n",
      "314 sin: Returns the sine (sin) of each values in a ra ...\n",
      "315 single_part_to_multi_part: Converts a vector file containing multi-part  ...\n",
      "316 sinh: Returns the hyperbolic sine (sinh) of each va ...\n",
      "317 sink: Identifies the depressions in a DEM, giving e ...\n",
      "318 slope: Calculates a slope raster from an input DEM. ...\n",
      "319 slope_vs_elevation_plot: Creates a slope vs. elevation plot for one or ...\n",
      "320 smooth_vectors: Smooths a vector coverage of either a POLYLIN ...\n",
      "321 snap_pour_points: Moves outlet points used to specify points of ...\n",
      "322 sobel_filter: Performs a Sobel edge-detection filter on an  ...\n",
      "323 split_colour_composite: This tool splits an RGB colour composite imag ...\n",
      "324 square: Squares the values in a raster. ...\n",
      "325 square_root: Returns the square root of the values in a ra ...\n",
      "326 standard_deviation_contrast_stretch: Performs a standard-deviation contrast stretc ...\n",
      "327 standard_deviation_filter: Assigns each cell in the output grid the stan ...\n",
      "328 standard_deviation_of_slope: Calculates the standard deviation of slope fr ...\n",
      "329 stochastic_depression_analysis: Preforms a stochastic analysis of depressions ...\n",
      "330 strahler_order_basins: Identifies Strahler-order basins from an inpu ...\n",
      "331 strahler_stream_order: Assigns the Strahler stream order to each lin ...\n",
      "332 stream_link_class: Identifies the exterior/interior links and no ...\n",
      "333 stream_link_identifier: Assigns a unique identifier to each link in a ...\n",
      "334 stream_link_length: Estimates the length of each link (or tributa ...\n",
      "335 stream_link_slope: Estimates the average slope of each link (or  ...\n",
      "336 stream_slope_continuous: Estimates the slope of each grid cell in a st ...\n",
      "337 subbasins: Identifies the catchments, or sub-basin, drai ...\n",
      "338 subtract: Performs a differencing operation on two rast ...\n",
      "339 sum_overlay: Calculates the sum for each grid cell from a  ...\n",
      "340 tin_gridding: Creates a raster grid based on a triangular i ...\n",
      "341 tan: Returns the tangent (tan) of each values in a ...\n",
      "342 tangential_curvature: Calculates a tangential curvature raster from ...\n",
      "343 tanh: Returns the hyperbolic tangent (tanh) of each ...\n",
      "344 thicken_raster_line: Thickens single-cell wide lines within a rast ...\n",
      "345 to_degrees: Converts a raster from radians to degrees. ...\n",
      "346 to_radians: Converts a raster from degrees to radians. ...\n",
      "347 tophat_transform: Performs either a white or black top-hat tran ...\n",
      "348 topological_stream_order: Assigns each link in a stream network its top ...\n",
      "349 total_curvature: Calculates a total curvature raster from an i ...\n",
      "350 total_filter: Performs a total filter on an input image. ...\n",
      "351 trace_downslope_flowpaths: Traces downslope flowpaths from one or more t ...\n",
      "352 trend_surface: Estimates the trend surface of an input raste ...\n",
      "353 trend_surface_vector_points: Estimates a trend surface from vector points. ...\n",
      "354 tributary_identifier: Assigns a unique identifier to each tributary ...\n",
      "355 truncate: Truncates the values in a raster to the desir ...\n",
      "356 turning_bands_simulation: Creates an image containing random values bas ...\n",
      "357 unnest_basins: Extract whole watersheds for a set of outlet  ...\n",
      "358 unsharp_masking: An image sharpening technique that enhances e ...\n",
      "359 user_defined_weights_filter: Performs a user-defined weights filter on an  ...\n",
      "360 vector_hex_binning: Hex-bins a set of vector points. ...\n",
      "361 vector_lines_to_raster: Converts a vector containing polylines into a ...\n",
      "362 vector_points_to_raster: Converts a vector containing points into a ra ...\n",
      "363 vector_polygons_to_raster: Converts a vector containing polygons into a  ...\n",
      "364 viewshed: Identifies the viewshed for a point or set of ...\n",
      "365 visibility_index: Estimates the relative visibility of sites in ...\n",
      "366 watershed: Identifies the watershed, or drainage basin,  ...\n",
      "367 weighted_overlay: Performs a weighted sum on multiple input ras ...\n",
      "368 weighted_sum: Performs a weighted-sum overlay on multiple i ...\n",
      "369 wetness_index: Calculates the topographic wetness index, Ln( ...\n",
      "370 write_function_memory_insertion: Performs a write function memory insertion fo ...\n",
      "371 xor: Performs a logical XOR operator on two Boolea ...\n",
      "372 z_scores: Standardizes the values in an input raster by ...\n"
     ]
    }
   ],
   "source": [
    "all_tools = wbt.list_tools()\n",
    "for index, tool in enumerate(all_tools):\n",
    "    print(\"{} {}: {} ...\".format(str(index+1).zfill(3), tool, all_tools[tool][:45]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section demonstrates two ways to get data into Binder so that you can test **whitebox** on the cloud using your own data. \n",
    "\n",
    "* [Getting data from direct URLs](#Getting-data-from-direct-URLs) \n",
    "* [Getting data from Google Drive](#Getting-data-from-Google-Drive)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting data from direct URLs\n",
    "\n",
    "If you have data hosted on your own HTTP server or GitHub, you should be able to get direct URLs. With a direct URL, users can automatically download the data when the URL is clicked. For example https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the following Python libraries and start getting data from direct URLs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pygis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a folder named *whitebox* under the user home folder and set it as the working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working directory: /home/jovyan/examples/data\n"
     ]
    }
   ],
   "source": [
    "work_dir = os.path.join(root_dir, 'data')\n",
    "if not os.path.exists(work_dir):\n",
    "    os.mkdir(work_dir)\n",
    "print(\"Working directory: {}\".format(os.path.realpath(work_dir)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace the following URL with your own direct URL hosting your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download data the from the above URL and unzip the file if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading testdata.zip ...\n",
      "Downloading done.\n",
      "Unzipping testdata.zip ...\n",
      "Unzipping done.\n",
      "Data directory: /home/jovyan/examples/data/testdata\n"
     ]
    }
   ],
   "source": [
    "pygis.download_from_url(url, out_dir=work_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have successfully downloaded data to Binder. Therefore, you can skip to [Using whitebox](#Using-whitebox) and start testing whitebox with your own data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting data from Google Drive\n",
    "\n",
    "Alternatively, you can upload data to [Google Drive](https://www.google.com/drive/) and then [share files publicly from Google Drive](https://support.google.com/drive/answer/2494822?co=GENIE.Platform%3DDesktop&hl=en). Once the file is shared publicly, you should be able to get a shareable URL. For example, https://drive.google.com/file/d/1xgxMLRh_jOLRNq-f3T_LXAaSuv9g_JnV."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Replace the following URL with your own shareable URL from Google Drive.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "gfile_url = 'https://drive.google.com/file/d/1xgxMLRh_jOLRNq-f3T_LXAaSuv9g_JnV'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Download the shared file from Google Drive.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Google Drive file id: 1xgxMLRh_jOLRNq-f3T_LXAaSuv9g_JnV\n",
      "Downloading 1xgxMLRh_jOLRNq-f3T_LXAaSuv9g_JnV into /home/jovyan/examples/data/testdata.zip... Done.\n",
      "Unzipping...Done.\n"
     ]
    }
   ],
   "source": [
    "pygis.download_from_gdrive(gfile_url, file_name='testdata.zip', out_dir=work_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have successfully downloaded data from Google Drive to Binder. You can now continue to [Using whitebox](#Using-whitebox) and start testing whitebox with your own data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using whitebox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can specify where your data are located. In this example, we will use [DEM.tif](https://github.com/giswqs/whitebox/blob/master/examples/testdata/DEM.tif), which has been downloaded to the testdata folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List data under the data folder.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.ipynb_checkpoints', 'DEM.dep', 'DEM.tif']\n"
     ]
    }
   ],
   "source": [
    "data_dir = os.path.join(root_dir, 'data/testdata/')\n",
    "print(os.listdir(data_dir))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this simple example, we smooth [DEM.tif](https://github.com/giswqs/whitebox/blob/master/examples/testdata/DEM.tif) using a [feature preserving denoising](https://github.com/jblindsay/whitebox-tools/blob/master/src/tools/terrain_analysis/feature_preserving_denoise.rs) algorithm. Then, we fill depressions in the DEM using a [depression breaching](https://github.com/jblindsay/whitebox-tools/blob/master/src/tools/hydro_analysis/breach_depressions.rs) algorithm. Finally, we calculate [flow accumulation](https://github.com/jblindsay/whitebox-tools/blob/master/src/tools/hydro_analysis/dinf_flow_accum.rs) based on the depressionless DEM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating normal vectors: 0.004417452S\n",
      "Smoothing normal vectors: 0.029072877S\n",
      "Updating elevations...\n",
      "Iteration 1 of 5...\n",
      "Iteration 2 of 5...\n",
      "Iteration 3 of 5...\n",
      "Iteration 4 of 5...\n",
      "Iteration 5 of 5...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import whitebox\n",
    "wbt = whitebox.WhiteboxTools()\n",
    "# set whitebox working directory\n",
    "wbt.set_working_dir(data_dir)\n",
    "wbt.verbose = False\n",
    "\n",
    "# call whiteboxtool\n",
    "wbt.feature_preserving_denoise(\"DEM.tif\", \"smoothed.tif\", filter=9)\n",
    "wbt.breach_depressions(\"smoothed.tif\", \"breached.tif\")\n",
    "wbt.d_inf_flow_accumulation(\"breached.tif\", \"flow_accum.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying results\n",
    "\n",
    "This section demonstrates how to display images on Jupyter Notebook. Three Python packages are used here, including [matplotlib](https://matplotlib.org/), [imageio](https://imageio.readthedocs.io/en/stable/installation.html), and [tifffile](https://pypi.org/project/tifffile/). These three packages can be installed using the following command:\n",
    "\n",
    "`pip install matplotlib imageio tifffile`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import the libraries.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comment out the third line (%matplotlib inline) if you run the tutorial in other IDEs other than Jupyter Notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import imageio\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Display one single image.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raster = imageio.imread(os.path.join(data_dir, 'DEM.tif'))\n",
    "plt.imshow(raster)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read images as numpy arrays.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "original = imageio.imread(os.path.join(data_dir, 'DEM.tif'))\n",
    "smoothed = imageio.imread(os.path.join(data_dir, 'smoothed.tif'))\n",
    "breached = imageio.imread(os.path.join(data_dir, 'breached.tif'))\n",
    "flow_accum = imageio.imread(os.path.join(data_dir, 'flow_accum.tif'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Display multiple images in one plot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,11))\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1)\n",
    "ax1.set_title('Original DEM')\n",
    "plt.imshow(original)\n",
    "\n",
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "ax2.set_title('Smoothed DEM')\n",
    "plt.imshow(smoothed)\n",
    "\n",
    "ax3 = fig.add_subplot(2, 2, 3)\n",
    "ax3.set_title('Breached DEM')\n",
    "plt.imshow(breached)\n",
    "\n",
    "ax4 = fig.add_subplot(2, 2, 4)\n",
    "ax4.set_title('Flow Accumulation')\n",
    "plt.imshow(flow_accum)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## whitebox GUI\n",
    "\n",
    "WhiteboxTools also provides a Graphical User Interface (GUI) - **WhiteboxTools Runner**, which can be invoked using the following Python script. *__Note that the GUI might not work in Jupyter notebooks deployed on the cloud (e.g., MyBinder.org), but it should work on Jupyter notebooks on local computers.__*\n",
    "\n",
    "```python\n",
    "import whitebox\n",
    "whitebox.Runner()\n",
    "\n",
    "```\n",
    "\n",
    "![](https://wetlands.io/file/images/whitebox.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Citing whitebox\n",
    "\n",
    "If you use the **whitebox** Python package for your research and publications, please consider citing the following papers to give Prof. [John Lindsay](http://www.uoguelph.ca/~hydrogeo/index.html) credits for his tremendous efforts in developing [Whitebox GAT](https://github.com/jblindsay/whitebox-geospatial-analysis-tools) and [WhiteboxTools](https://github.com/jblindsay/whitebox-tools). Without his work, this **whitebox** Python package would not exist!  \n",
    "\n",
    "* Lindsay, J. B. (2016). Whitebox GAT: A case study in geomorphometric analysis. Computers & Geosciences, 95, 75-84. http://dx.doi.org/10.1016/j.cageo.2016.07.003"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Credits\n",
    "\n",
    "This interactive notebook is made possible by [MyBinder.org](https://mybinder.org/). Big thanks to [MyBinder.org](https://mybinder.org/) for developing the amazing binder platform, which is extremely valuable for reproducible research!\n",
    "\n",
    "This tutorial made use a number of open-source Python packages, including [ Cookiecutter](https://github.com/audreyr/cookiecutter), [numpy](http://www.numpy.org/), [matplotlib](https://matplotlib.org/), [imageio](https://imageio.readthedocs.io/en/stable/installation.html), [tifffile](https://pypi.org/project/tifffile/), and [google-drive-downloader](https://github.com/ndrplz/google-drive-downloader). Thanks to all developers of these wonderful Python packages!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contact\n",
    "\n",
    "If you have any questions regarding this tutorial or the **whitebox** Python package, you can contact me (Dr. Qiusheng Wu) at wqs@binghamton.edu or https://wetlands.io/#contact"
   ]
  }
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